/**
* This file is part of ORB-SLAM2.
* This file is based on the file orb.cpp from the OpenCV library (see BSD license below).
*
* Copyright (C) 2014-2016 Raúl Mur-Artal <raulmur at unizar dot es> (University of Zaragoza)
* For more information see <https://github.com/raulmur/ORB_SLAM2>
*
* ORB-SLAM2 is free software: you can redistribute it and/or modify
* it under the terms of the GNU General Public License as published by
* the Free Software Foundation, either version 3 of the License, or
* (at your option) any later version.
*
* ORB-SLAM2 is distributed in the hope that it will be useful,
* but WITHOUT ANY WARRANTY; without even the implied warranty of
* MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
* GNU General Public License for more details.
*
* You should have received a copy of the GNU General Public License
* along with ORB-SLAM2. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Software License Agreement (BSD License)
*
*  Copyright (c) 2009, Willow Garage, Inc.
*  All rights reserved.
*
*  Redistribution and use in source and binary forms, with or without
*  modification, are permitted provided that the following conditions
*  are met:
*
*   * Redistributions of source code must retain the above copyright
*     notice, this list of conditions and the following disclaimer.
*   * Redistributions in binary form must reproduce the above
*     copyright notice, this list of conditions and the following
*     disclaimer in the documentation and/or other materials provided
*     with the distribution.
*   * Neither the name of the Willow Garage nor the names of its
*     contributors may be used to endorse or promote products derived
*     from this software without specific prior written permission.
*
*  THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
*  "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
*  LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS
*  FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE
*  COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT,
*  INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING,
*  BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
*  LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
*  CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
*  LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN
*  ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE
*  POSSIBILITY OF SUCH DAMAGE.
*
*/


#include <opencv2/core/core.hpp>
#include <opencv2/highgui/highgui.hpp>
#include <opencv2/features2d/features2d.hpp>
#include <opencv2/imgproc/imgproc.hpp>
#include <vector>
#include <iostream>

#include "ORBextractor.h"


using namespace cv;
using namespace std;

namespace ORB_SLAM2
{

  const int PATCH_SIZE      = 31;
  const int HALF_PATCH_SIZE = 15;
  const int EDGE_THRESHOLD  = 19;


  static float IC_Angle(const Mat& image, Point2f pt, const vector<int>& u_max)
  {
    int m_01 = 0, m_10 = 0;

    const uchar *center = &image.at<uchar>(cvRound(pt.y), cvRound(pt.x));

    // Treat the center line differently, v=0
    for(int u = -HALF_PATCH_SIZE; u <= HALF_PATCH_SIZE; ++u)
      m_10 += u * center[u];

    // Go line by line in the circuI853lar patch
    int     step = (int) image.step1();
    for(int v    = 1; v <= HALF_PATCH_SIZE; ++v)
    {
      // Proceed over the two lines
      int     v_sum = 0;
      int     d     = u_max[v];
      for(int u     = -d; u <= d; ++u)
      {
        int val_plus = center[u + v * step], val_minus = center[u - v * step];
        v_sum += (val_plus - val_minus);
        m_10 += u * (val_plus + val_minus);
      }
      m_01 += v * v_sum;
    }

    return fastAtan2((float) m_01, (float) m_10);
  }


  const float factorPI = (float) (CV_PI / 180.f);
  static void computeOrbDescriptor(const KeyPoint& kpt, const Mat& img, const Point *pattern, uchar *desc)
  {
    float angle = (float) kpt.angle * factorPI;
    float a     = (float) cos(angle), b = (float) sin(angle);

    const uchar *center = &img.at<uchar>(cvRound(kpt.pt.y), cvRound(kpt.pt.x));
    const int step = (int) img.step;

#define GET_VALUE(idx) \
        center[cvRound(pattern[idx].x*b + pattern[idx].y*a)*step + \
               cvRound(pattern[idx].x*a - pattern[idx].y*b)]


    for(int i = 0; i < 32; ++i, pattern += 16)
    {
      int t0, t1, val;
      t0 = GET_VALUE(0);
      t1 = GET_VALUE(1);
      val = t0 < t1;
      t0  = GET_VALUE(2);
      t1 = GET_VALUE(3);
      val |= (t0 < t1) << 1;
      t0 = GET_VALUE(4);
      t1 = GET_VALUE(5);
      val |= (t0 < t1) << 2;
      t0 = GET_VALUE(6);
      t1 = GET_VALUE(7);
      val |= (t0 < t1) << 3;
      t0 = GET_VALUE(8);
      t1 = GET_VALUE(9);
      val |= (t0 < t1) << 4;
      t0 = GET_VALUE(10);
      t1 = GET_VALUE(11);
      val |= (t0 < t1) << 5;
      t0 = GET_VALUE(12);
      t1 = GET_VALUE(13);
      val |= (t0 < t1) << 6;
      t0 = GET_VALUE(14);
      t1 = GET_VALUE(15);
      val |= (t0 < t1) << 7;

      desc[i] = (uchar) val;
    }

#undef GET_VALUE
  }


  static int bit_pattern_31_[256 * 4] = {8, -3, 9, 5/*mean (0), correlation (0)*/, 4, 2, 7,
                                         -12/*mean (1.12461e-05), correlation (0.0437584)*/, -11, 9, -8,
                                         2/*mean (3.37382e-05), correlation (0.0617409)*/, 7, -12, 12,
                                         -13/*mean (5.62303e-05), correlation (0.0636977)*/, 2, -13, 2,
                                         12/*mean (0.000134953), correlation (0.085099)*/, 1, -7, 1,
                                         6/*mean (0.000528565), correlation (0.0857175)*/, -2, -10, -2,
                                         -4/*mean (0.0188821), correlation (0.0985774)*/, -13, -13, -11,
                                         -8/*mean (0.0363135), correlation (0.0899616)*/, -13, -3, -12,
                                         -9/*mean (0.121806), correlation (0.099849)*/, 10, 4, 11,
                                         9/*mean (0.122065), correlation (0.093285)*/, -13, -8, -8,
                                         -9/*mean (0.162787), correlation (0.0942748)*/, -11, 7, -9,
                                         12/*mean (0.21561), correlation (0.0974438)*/, 7, 7, 12,
                                         6/*mean (0.160583), correlation (0.130064)*/, -4, -5, -3,
                                         0/*mean (0.228171), correlation (0.132998)*/, -13, 2, -12,
                                         -3/*mean (0.00997526), correlation (0.145926)*/, -9, 0, -7,
                                         5/*mean (0.198234), correlation (0.143636)*/, 12, -6, 12,
                                         -1/*mean (0.0676226), correlation (0.16689)*/, -3, 6, -2,
                                         12/*mean (0.166847), correlation (0.171682)*/, -6, -13, -4,
                                         -8/*mean (0.101215), correlation (0.179716)*/, 11, -13, 12,
                                         -8/*mean (0.200641), correlation (0.192279)*/, 4, 7, 5,
                                         1/*mean (0.205106), correlation (0.186848)*/, 5, -3, 10,
                                         -3/*mean (0.234908), correlation (0.192319)*/, 3, -7, 6,
                                         12/*mean (0.0709964), correlation (0.210872)*/, -8, -7, -6,
                                         -2/*mean (0.0939834), correlation (0.212589)*/, -2, 11, -1,
                                         -10/*mean (0.127778), correlation (0.20866)*/, -13, 12, -8,
                                         10/*mean (0.14783), correlation (0.206356)*/, -7, 3, -5,
                                         -3/*mean (0.182141), correlation (0.198942)*/, -4, 2, -3,
                                         7/*mean (0.188237), correlation (0.21384)*/, -10, -12, -6,
                                         11/*mean (0.14865), correlation (0.23571)*/, 5, -12, 6,
                                         -7/*mean (0.222312), correlation (0.23324)*/, 5, -6, 7,
                                         -1/*mean (0.229082), correlation (0.23389)*/, 1, 0, 4,
                                         -5/*mean (0.241577), correlation (0.215286)*/, 9, 11, 11,
                                         -13/*mean (0.00338507), correlation (0.251373)*/, 4, 7, 4,
                                         12/*mean (0.131005), correlation (0.257622)*/, 2, -1, 4,
                                         4/*mean (0.152755), correlation (0.255205)*/, -4, -12, -2,
                                         7/*mean (0.182771), correlation (0.244867)*/, -8, -5, -7,
                                         -10/*mean (0.186898), correlation (0.23901)*/, 4, 11, 9,
                                         12/*mean (0.226226), correlation (0.258255)*/, 0, -8, 1,
                                         -13/*mean (0.0897886), correlation (0.274827)*/, -13, -2, -8,
                                         2/*mean (0.148774), correlation (0.28065)*/, -3, -2, -2,
                                         3/*mean (0.153048), correlation (0.283063)*/, -6, 9, -4,
                                         -9/*mean (0.169523), correlation (0.278248)*/, 8, 12, 10,
                                         7/*mean (0.225337), correlation (0.282851)*/, 0, 9, 1,
                                         3/*mean (0.226687), correlation (0.278734)*/, 7, -5, 11,
                                         -10/*mean (0.00693882), correlation (0.305161)*/, -13, -6, -11,
                                         0/*mean (0.0227283), correlation (0.300181)*/, 10, 7, 12,
                                         1/*mean (0.125517), correlation (0.31089)*/, -6, -3, -6,
                                         12/*mean (0.131748), correlation (0.312779)*/, 10, -9, 12,
                                         -4/*mean (0.144827), correlation (0.292797)*/, -13, 8, -8,
                                         -12/*mean (0.149202), correlation (0.308918)*/, -13, 0, -8,
                                         -4/*mean (0.160909), correlation (0.310013)*/, 3, 3, 7,
                                         8/*mean (0.177755), correlation (0.309394)*/, 5, 7, 10,
                                         -7/*mean (0.212337), correlation (0.310315)*/, -1, 7, 1,
                                         -12/*mean (0.214429), correlation (0.311933)*/, 3, -10, 5,
                                         6/*mean (0.235807), correlation (0.313104)*/, 2, -4, 3,
                                         -10/*mean (0.00494827), correlation (0.344948)*/, -13, 0, -13,
                                         5/*mean (0.0549145), correlation (0.344675)*/, -13, -7, -12,
                                         12/*mean (0.103385), correlation (0.342715)*/, -13, 3, -11,
                                         8/*mean (0.134222), correlation (0.322922)*/, -7, 12, -4,
                                         7/*mean (0.153284), correlation (0.337061)*/, 6, -10, 12,
                                         8/*mean (0.154881), correlation (0.329257)*/, -9, -1, -7,
                                         -6/*mean (0.200967), correlation (0.33312)*/, -2, -5, 0,
                                         12/*mean (0.201518), correlation (0.340635)*/, -12, 5, -7,
                                         5/*mean (0.207805), correlation (0.335631)*/, 3, -10, 8,
                                         -13/*mean (0.224438), correlation (0.34504)*/, -7, -7, -4,
                                         5/*mean (0.239361), correlation (0.338053)*/, -3, -2, -1,
                                         -7/*mean (0.240744), correlation (0.344322)*/, 2, 9, 5,
                                         -11/*mean (0.242949), correlation (0.34145)*/, -11, -13, -5,
                                         -13/*mean (0.244028), correlation (0.336861)*/, -1, 6, 0,
                                         -1/*mean (0.247571), correlation (0.343684)*/, 5, -3, 5,
                                         2/*mean (0.000697256), correlation (0.357265)*/, -4, -13, -4,
                                         12/*mean (0.00213675), correlation (0.373827)*/, -9, -6, -9,
                                         6/*mean (0.0126856), correlation (0.373938)*/, -12, -10, -8,
                                         -4/*mean (0.0152497), correlation (0.364237)*/, 10, 2, 12,
                                         -3/*mean (0.0299933), correlation (0.345292)*/, 7, 12, 12,
                                         12/*mean (0.0307242), correlation (0.366299)*/, -7, -13, -6,
                                         5/*mean (0.0534975), correlation (0.368357)*/, -4, 9, -3,
                                         4/*mean (0.099865), correlation (0.372276)*/, 7, -1, 12,
                                         2/*mean (0.117083), correlation (0.364529)*/, -7, 6, -5,
                                         1/*mean (0.126125), correlation (0.369606)*/, -13, 11, -12,
                                         5/*mean (0.130364), correlation (0.358502)*/, -3, 7, -2,
                                         -6/*mean (0.131691), correlation (0.375531)*/, 7, -8, 12,
                                         -7/*mean (0.160166), correlation (0.379508)*/, -13, -7, -11,
                                         -12/*mean (0.167848), correlation (0.353343)*/, 1, -3, 12,
                                         12/*mean (0.183378), correlation (0.371916)*/, 2, -6, 3,
                                         0/*mean (0.228711), correlation (0.371761)*/, -4, 3, -2,
                                         -13/*mean (0.247211), correlation (0.364063)*/, -1, -13, 1,
                                         9/*mean (0.249325), correlation (0.378139)*/, 7, 1, 8,
                                         -6/*mean (0.000652272), correlation (0.411682)*/, 1, -1, 3,
                                         12/*mean (0.00248538), correlation (0.392988)*/, 9, 1, 12,
                                         6/*mean (0.0206815), correlation (0.386106)*/, -1, -9, -1,
                                         3/*mean (0.0364485), correlation (0.410752)*/, -13, -13, -10,
                                         5/*mean (0.0376068), correlation (0.398374)*/, 7, 7, 10,
                                         12/*mean (0.0424202), correlation (0.405663)*/, 12, -5, 12,
                                         9/*mean (0.0942645), correlation (0.410422)*/, 6, 3, 7,
                                         11/*mean (0.1074), correlation (0.413224)*/, 5, -13, 6,
                                         10/*mean (0.109256), correlation (0.408646)*/, 2, -12, 2,
                                         3/*mean (0.131691), correlation (0.416076)*/, 3, 8, 4,
                                         -6/*mean (0.165081), correlation (0.417569)*/, 2, 6, 12,
                                         -13/*mean (0.171874), correlation (0.408471)*/, 9, -12, 10,
                                         3/*mean (0.175146), correlation (0.41296)*/, -8, 4, -7,
                                         9/*mean (0.183682), correlation (0.402956)*/, -11, 12, -4,
                                         -6/*mean (0.184672), correlation (0.416125)*/, 1, 12, 2,
                                         -8/*mean (0.191487), correlation (0.386696)*/, 6, -9, 7,
                                         -4/*mean (0.192668), correlation (0.394771)*/, 2, 3, 3,
                                         -2/*mean (0.200157), correlation (0.408303)*/, 6, 3, 11,
                                         0/*mean (0.204588), correlation (0.411762)*/, 3, -3, 8,
                                         -8/*mean (0.205904), correlation (0.416294)*/, 7, 8, 9,
                                         3/*mean (0.213237), correlation (0.409306)*/, -11, -5, -6,
                                         -4/*mean (0.243444), correlation (0.395069)*/, -10, 11, -5,
                                         10/*mean (0.247672), correlation (0.413392)*/, -5, -8, -3,
                                         12/*mean (0.24774), correlation (0.411416)*/, -10, 5, -9,
                                         0/*mean (0.00213675), correlation (0.454003)*/, 8, -1, 12,
                                         -6/*mean (0.0293635), correlation (0.455368)*/, 4, -6, 6,
                                         -11/*mean (0.0404971), correlation (0.457393)*/, -10, 12, -8,
                                         7/*mean (0.0481107), correlation (0.448364)*/, 4, -2, 6,
                                         7/*mean (0.050641), correlation (0.455019)*/, -2, 0, -2,
                                         12/*mean (0.0525978), correlation (0.44338)*/, -5, -8, -5,
                                         2/*mean (0.0629667), correlation (0.457096)*/, 7, -6, 10,
                                         12/*mean (0.0653846), correlation (0.445623)*/, -9, -13, -8,
                                         -8/*mean (0.0858749), correlation (0.449789)*/, -5, -13, -5,
                                         -2/*mean (0.122402), correlation (0.450201)*/, 8, -8, 9,
                                         -13/*mean (0.125416), correlation (0.453224)*/, -9, -11, -9,
                                         0/*mean (0.130128), correlation (0.458724)*/, 1, -8, 1,
                                         -2/*mean (0.132467), correlation (0.440133)*/, 7, -4, 9,
                                         1/*mean (0.132692), correlation (0.454)*/, -2, 1, -1,
                                         -4/*mean (0.135695), correlation (0.455739)*/, 11, -6, 12,
                                         -11/*mean (0.142904), correlation (0.446114)*/, -12, -9, -6,
                                         4/*mean (0.146165), correlation (0.451473)*/, 3, 7, 7,
                                         12/*mean (0.147627), correlation (0.456643)*/, 5, 5, 10,
                                         8/*mean (0.152901), correlation (0.455036)*/, 0, -4, 2,
                                         8/*mean (0.167083), correlation (0.459315)*/, -9, 12, -5,
                                         -13/*mean (0.173234), correlation (0.454706)*/, 0, 7, 2,
                                         12/*mean (0.18312), correlation (0.433855)*/, -1, 2, 1,
                                         7/*mean (0.185504), correlation (0.443838)*/, 5, 11, 7,
                                         -9/*mean (0.185706), correlation (0.451123)*/, 3, 5, 6,
                                         -8/*mean (0.188968), correlation (0.455808)*/, -13, -4, -8,
                                         9/*mean (0.191667), correlation (0.459128)*/, -5, 9, -3,
                                         -3/*mean (0.193196), correlation (0.458364)*/, -4, -7, -3,
                                         -12/*mean (0.196536), correlation (0.455782)*/, 6, 5, 8,
                                         0/*mean (0.1972), correlation (0.450481)*/, -7, 6, -6,
                                         12/*mean (0.199438), correlation (0.458156)*/, -13, 6, -5,
                                         -2/*mean (0.211224), correlation (0.449548)*/, 1, -10, 3,
                                         10/*mean (0.211718), correlation (0.440606)*/, 4, 1, 8,
                                         -4/*mean (0.213034), correlation (0.443177)*/, -2, -2, 2,
                                         -13/*mean (0.234334), correlation (0.455304)*/, 2, -12, 12,
                                         12/*mean (0.235684), correlation (0.443436)*/, -2, -13, 0,
                                         -6/*mean (0.237674), correlation (0.452525)*/, 4, 1, 9,
                                         3/*mean (0.23962), correlation (0.444824)*/, -6, -10, -3,
                                         -5/*mean (0.248459), correlation (0.439621)*/, -3, -13, -1,
                                         1/*mean (0.249505), correlation (0.456666)*/, 7, 5, 12,
                                         -11/*mean (0.00119208), correlation (0.495466)*/, 4, -2, 5,
                                         -7/*mean (0.00372245), correlation (0.484214)*/, -13, 9, -9,
                                         -5/*mean (0.00741116), correlation (0.499854)*/, 7, 1, 8,
                                         6/*mean (0.0208952), correlation (0.499773)*/, 7, -8, 7,
                                         6/*mean (0.0220085), correlation (0.501609)*/, -7, -4, -7,
                                         1/*mean (0.0233806), correlation (0.496568)*/, -8, 11, -7,
                                         -8/*mean (0.0236505), correlation (0.489719)*/, -13, 6, -12,
                                         -8/*mean (0.0268781), correlation (0.503487)*/, 2, 4, 3,
                                         9/*mean (0.0323324), correlation (0.501938)*/, 10, -5, 12,
                                         3/*mean (0.0399235), correlation (0.494029)*/, -6, -5, -6,
                                         7/*mean (0.0420153), correlation (0.486579)*/, 8, -3, 9,
                                         -8/*mean (0.0548021), correlation (0.484237)*/, 2, -12, 2,
                                         8/*mean (0.0616622), correlation (0.496642)*/, -11, -2, -10,
                                         3/*mean (0.0627755), correlation (0.498563)*/, -12, -13, -7,
                                         -9/*mean (0.0829622), correlation (0.495491)*/, -11, 0, -10,
                                         -5/*mean (0.0843342), correlation (0.487146)*/, 5, -3, 11,
                                         8/*mean (0.0929937), correlation (0.502315)*/, -2, -13, -1,
                                         12/*mean (0.113327), correlation (0.48941)*/, -1, -8, 0,
                                         9/*mean (0.132119), correlation (0.467268)*/, -13, -11, -12,
                                         -5/*mean (0.136269), correlation (0.498771)*/, -10, -2, -10,
                                         11/*mean (0.142173), correlation (0.498714)*/, -3, 9, -2,
                                         -13/*mean (0.144141), correlation (0.491973)*/, 2, -3, 3,
                                         2/*mean (0.14892), correlation (0.500782)*/, -9, -13, -4,
                                         0/*mean (0.150371), correlation (0.498211)*/, -4, 6, -3,
                                         -10/*mean (0.152159), correlation (0.495547)*/, -4, 12, -2,
                                         -7/*mean (0.156152), correlation (0.496925)*/, -6, -11, -4,
                                         9/*mean (0.15749), correlation (0.499222)*/, 6, -3, 6,
                                         11/*mean (0.159211), correlation (0.503821)*/, -13, 11, -5,
                                         5/*mean (0.162427), correlation (0.501907)*/, 11, 11, 12,
                                         6/*mean (0.16652), correlation (0.497632)*/, 7, -5, 12,
                                         -2/*mean (0.169141), correlation (0.484474)*/, -1, 12, 0,
                                         7/*mean (0.169456), correlation (0.495339)*/, -4, -8, -3,
                                         -2/*mean (0.171457), correlation (0.487251)*/, -7, 1, -6,
                                         7/*mean (0.175), correlation (0.500024)*/, -13, -12, -8,
                                         -13/*mean (0.175866), correlation (0.497523)*/, -7, -2, -6,
                                         -8/*mean (0.178273), correlation (0.501854)*/, -8, 5, -6,
                                         -9/*mean (0.181107), correlation (0.494888)*/, -5, -1, -4,
                                         5/*mean (0.190227), correlation (0.482557)*/, -13, 7, -8,
                                         10/*mean (0.196739), correlation (0.496503)*/, 1, 5, 5,
                                         -13/*mean (0.19973), correlation (0.499759)*/, 1, 0, 10,
                                         -13/*mean (0.204465), correlation (0.49873)*/, 9, 12, 10,
                                         -1/*mean (0.209334), correlation (0.49063)*/, 5, -8, 10,
                                         -9/*mean (0.211134), correlation (0.503011)*/, -1, 11, 1,
                                         -13/*mean (0.212), correlation (0.499414)*/, -9, -3, -6,
                                         2/*mean (0.212168), correlation (0.480739)*/, -1, -10, 1,
                                         12/*mean (0.212731), correlation (0.502523)*/, -13, 1, -8,
                                         -10/*mean (0.21327), correlation (0.489786)*/, 8, -11, 10,
                                         -6/*mean (0.214159), correlation (0.488246)*/, 2, -13, 3,
                                         -6/*mean (0.216993), correlation (0.50287)*/, 7, -13, 12,
                                         -9/*mean (0.223639), correlation (0.470502)*/, -10, -10, -5,
                                         -7/*mean (0.224089), correlation (0.500852)*/, -10, -8, -8,
                                         -13/*mean (0.228666), correlation (0.502629)*/, 4, -6, 8,
                                         5/*mean (0.22906), correlation (0.498305)*/, 3, 12, 8,
                                         -13/*mean (0.233378), correlation (0.503825)*/, -4, 2, -3,
                                         -3/*mean (0.234323), correlation (0.476692)*/, 5, -13, 10,
                                         -12/*mean (0.236392), correlation (0.475462)*/, 4, -13, 5,
                                         -1/*mean (0.236842), correlation (0.504132)*/, -9, 9, -4,
                                         3/*mean (0.236977), correlation (0.497739)*/, 0, 3, 3,
                                         -9/*mean (0.24314), correlation (0.499398)*/, -12, 1, -6,
                                         1/*mean (0.243297), correlation (0.489447)*/, 3, 2, 4,
                                         -8/*mean (0.00155196), correlation (0.553496)*/, -10, -10, -10,
                                         9/*mean (0.00239541), correlation (0.54297)*/, 8, -13, 12,
                                         12/*mean (0.0034413), correlation (0.544361)*/, -8, -12, -6,
                                         -5/*mean (0.003565), correlation (0.551225)*/, 2, 2, 3,
                                         7/*mean (0.00835583), correlation (0.55285)*/, 10, 6, 11,
                                         -8/*mean (0.00885065), correlation (0.540913)*/, 6, 8, 8,
                                         -12/*mean (0.0101552), correlation (0.551085)*/, -7, 10, -6,
                                         5/*mean (0.0102227), correlation (0.533635)*/, -3, -9, -3,
                                         9/*mean (0.0110211), correlation (0.543121)*/, -1, -13, -1,
                                         5/*mean (0.0113473), correlation (0.550173)*/, -3, -7, -3,
                                         4/*mean (0.0140913), correlation (0.554774)*/, -8, -2, -8,
                                         3/*mean (0.017049), correlation (0.55461)*/, 4, 2, 12,
                                         12/*mean (0.01778), correlation (0.546921)*/, 2, -5, 3,
                                         11/*mean (0.0224022), correlation (0.549667)*/, 6, -9, 11,
                                         -13/*mean (0.029161), correlation (0.546295)*/, 3, -1, 7,
                                         12/*mean (0.0303081), correlation (0.548599)*/, 11, -1, 12,
                                         4/*mean (0.0355151), correlation (0.523943)*/, -3, 0, -3,
                                         6/*mean (0.0417904), correlation (0.543395)*/, 4, -11, 4,
                                         12/*mean (0.0487292), correlation (0.542818)*/, 2, -4, 2,
                                         1/*mean (0.0575124), correlation (0.554888)*/, -10, -6, -8,
                                         1/*mean (0.0594242), correlation (0.544026)*/, -13, 7, -11,
                                         1/*mean (0.0597391), correlation (0.550524)*/, -13, 12, -11,
                                         -13/*mean (0.0608974), correlation (0.55383)*/, 6, 0, 11,
                                         -13/*mean (0.065126), correlation (0.552006)*/, 0, -1, 1,
                                         4/*mean (0.074224), correlation (0.546372)*/, -13, 3, -9,
                                         -2/*mean (0.0808592), correlation (0.554875)*/, -9, 8, -6,
                                         -3/*mean (0.0883378), correlation (0.551178)*/, -13, -6, -8,
                                         -2/*mean (0.0901035), correlation (0.548446)*/, 5, -9, 8,
                                         10/*mean (0.0949843), correlation (0.554694)*/, 2, 7, 3,
                                         -9/*mean (0.0994152), correlation (0.550979)*/, -1, -6, -1,
                                         -1/*mean (0.10045), correlation (0.552714)*/, 9, 5, 11,
                                         -2/*mean (0.100686), correlation (0.552594)*/, 11, -3, 12,
                                         -8/*mean (0.101091), correlation (0.532394)*/, 3, 0, 3,
                                         5/*mean (0.101147), correlation (0.525576)*/, -1, 4, 0,
                                         10/*mean (0.105263), correlation (0.531498)*/, 3, -6, 4,
                                         5/*mean (0.110785), correlation (0.540491)*/, -13, 0, -10,
                                         5/*mean (0.112798), correlation (0.536582)*/, 5, 8, 12,
                                         11/*mean (0.114181), correlation (0.555793)*/, 8, 9, 9,
                                         -6/*mean (0.117431), correlation (0.553763)*/, 7, -4, 8,
                                         -12/*mean (0.118522), correlation (0.553452)*/, -10, 4, -10,
                                         9/*mean (0.12094), correlation (0.554785)*/, 7, 3, 12,
                                         4/*mean (0.122582), correlation (0.555825)*/, 9, -7, 10,
                                         -2/*mean (0.124978), correlation (0.549846)*/, 7, 0, 12,
                                         -2/*mean (0.127002), correlation (0.537452)*/, -1, -6, 0,
                                         -11/*mean (0.127148), correlation (0.547401)*/
  };

  ORBextractor::ORBextractor(int _nfeatures, float _scaleFactor, int _nlevels, int _iniThFAST, int _minThFAST)
    : nfeatures(_nfeatures), scaleFactor(_scaleFactor), nlevels(_nlevels), iniThFAST(_iniThFAST), minThFAST(_minThFAST)
  {
    mvScaleFactor.resize(nlevels);
    mvLevelSigma2.resize(nlevels);
    mvScaleFactor[0] = 1.0f;
    mvLevelSigma2[0] = 1.0f;
    for(int i = 1; i < nlevels; i++)
    {
      mvScaleFactor[i] = mvScaleFactor[i - 1] * scaleFactor;
      mvLevelSigma2[i] = mvScaleFactor[i] * mvScaleFactor[i];
    }

    mvInvScaleFactor.resize(nlevels);
    mvInvLevelSigma2.resize(nlevels);
    for(int i = 0; i < nlevels; i++)
    {
      mvInvScaleFactor[i] = 1.0f / mvScaleFactor[i];
      mvInvLevelSigma2[i] = 1.0f / mvLevelSigma2[i];
    }

    mvImagePyramid.resize(nlevels);

    mnFeaturesPerLevel.resize(nlevels);
    float factor                   = 1.0f / scaleFactor;
    float nDesiredFeaturesPerScale = nfeatures * (1 - factor) / (1 - (float) pow((double) factor, (double) nlevels));

    int     sumFeatures = 0;
    for(int level       = 0; level < nlevels - 1; level++)
    {
      mnFeaturesPerLevel[level] = cvRound(nDesiredFeaturesPerScale);
      sumFeatures += mnFeaturesPerLevel[level];
      nDesiredFeaturesPerScale *= factor;
    }
    mnFeaturesPerLevel[nlevels - 1] = std::max(nfeatures - sumFeatures, 0);

    const int npoints = 512;
    const Point *pattern0    = (const Point *) bit_pattern_31_;
    std::copy(pattern0, pattern0 + npoints, std::back_inserter(pattern));

    //This is for orientation
    // pre-compute the end of a row in a circular patch
    umax.resize(HALF_PATCH_SIZE + 1);

    int          v, v0, vmax = cvFloor(HALF_PATCH_SIZE * sqrt(2.f) / 2 + 1);
    int          vmin        = cvCeil(HALF_PATCH_SIZE * sqrt(2.f) / 2);
    const double hp2         = HALF_PATCH_SIZE * HALF_PATCH_SIZE;
    for(v = 0; v <= vmax; ++v)
      umax[v] = cvRound(sqrt(hp2 - v * v));

    // Make sure we are symmetric
    for(v = HALF_PATCH_SIZE, v0 = 0; v >= vmin; --v)
    {
      while(umax[v0] == umax[v0 + 1])
        ++v0;
      umax[v] = v0;
      ++v0;
    }
  }

  static void computeOrientation(const Mat& image, vector<KeyPoint>& keypoints, const vector<int>& umax)
  {
    for(vector<KeyPoint>::iterator keypoint = keypoints.begin(), keypointEnd = keypoints.end(); keypoint != keypointEnd;
        ++keypoint)
    {
      keypoint->angle = IC_Angle(image, keypoint->pt, umax);
    }
  }

  void ExtractorNode::DivideNode(ExtractorNode& n1, ExtractorNode& n2, ExtractorNode& n3, ExtractorNode& n4)
  {
    const int halfX = ceil(static_cast<float>(UR.x - UL.x) / 2);
    const int halfY = ceil(static_cast<float>(BR.y - UL.y) / 2);

    //Define boundaries of childs
    n1.UL = UL;
    n1.UR = cv::Point2i(UL.x + halfX, UL.y);
    n1.BL = cv::Point2i(UL.x, UL.y + halfY);
    n1.BR = cv::Point2i(UL.x + halfX, UL.y + halfY);
    n1.vKeys.reserve(vKeys.size());

    n2.UL = n1.UR;
    n2.UR = UR;
    n2.BL = n1.BR;
    n2.BR = cv::Point2i(UR.x, UL.y + halfY);
    n2.vKeys.reserve(vKeys.size());

    n3.UL = n1.BL;
    n3.UR = n1.BR;
    n3.BL = BL;
    n3.BR = cv::Point2i(n1.BR.x, BL.y);
    n3.vKeys.reserve(vKeys.size());

    n4.UL = n3.UR;
    n4.UR = n2.BR;
    n4.BL = n3.BR;
    n4.BR = BR;
    n4.vKeys.reserve(vKeys.size());

    //Associate points to childs
    for(size_t i = 0; i < vKeys.size(); i++)
    {
      const cv::KeyPoint& kp = vKeys[i];
      if(kp.pt.x < n1.UR.x)
      {
        if(kp.pt.y < n1.BR.y)
          n1.vKeys.push_back(kp);
        else
          n3.vKeys.push_back(kp);
      }
      else if(kp.pt.y < n1.BR.y)
        n2.vKeys.push_back(kp);
      else
        n4.vKeys.push_back(kp);
    }

    if(n1.vKeys.size() == 1)
      n1.bNoMore = true;
    if(n2.vKeys.size() == 1)
      n2.bNoMore = true;
    if(n3.vKeys.size() == 1)
      n3.bNoMore = true;
    if(n4.vKeys.size() == 1)
      n4.bNoMore = true;

  }

  vector<cv::KeyPoint> ORBextractor::DistributeOctTree(const vector<cv::KeyPoint>& vToDistributeKeys,
                                                       const int& minX,
                                                       const int& maxX,
                                                       const int& minY,
                                                       const int& maxY,
                                                       const int& N,
                                                       const int& level)
  {
    // Compute how many initial nodes   
    const int nIni = round(static_cast<float>(maxX - minX) / (maxY - minY));

    const float hX = static_cast<float>(maxX - minX) / nIni;

    list<ExtractorNode> lNodes;

    vector<ExtractorNode *> vpIniNodes;
    vpIniNodes.resize(nIni);

    for(int i = 0; i < nIni; i++)
    {
      ExtractorNode ni;
      ni.UL = cv::Point2i(hX * static_cast<float>(i), 0);
      ni.UR = cv::Point2i(hX * static_cast<float>(i + 1), 0);
      ni.BL = cv::Point2i(ni.UL.x, maxY - minY);
      ni.BR = cv::Point2i(ni.UR.x, maxY - minY);
      ni.vKeys.reserve(vToDistributeKeys.size());

      lNodes.push_back(ni);
      vpIniNodes[i] = &lNodes.back();
    }

    //Associate points to childs
    for(size_t i = 0; i < vToDistributeKeys.size(); i++)
    {
      const cv::KeyPoint& kp = vToDistributeKeys[i];
      vpIniNodes[kp.pt.x / hX]->vKeys.push_back(kp);
    }

    list<ExtractorNode>::iterator lit = lNodes.begin();

    while(lit != lNodes.end())
    {
      if(lit->vKeys.size() == 1)
      {
        lit->bNoMore = true;
        lit++;
      }
      else if(lit->vKeys.empty())
        lit = lNodes.erase(lit);
      else
        lit++;
    }

    bool bFinish = false;

    int iteration = 0;

    vector<pair<int, ExtractorNode *> > vSizeAndPointerToNode;
    vSizeAndPointerToNode.reserve(lNodes.size() * 4);

    while(!bFinish)
    {
      iteration++;

      int prevSize = lNodes.size();

      lit = lNodes.begin();

      int nToExpand = 0;

      vSizeAndPointerToNode.clear();

      while(lit != lNodes.end())
      {
        if(lit->bNoMore)
        {
          // If node only contains one point do not subdivide and continue
          lit++;
          continue;
        }
        else
        {
          // If more than one point, subdivide
          ExtractorNode n1, n2, n3, n4;
          lit->DivideNode(n1, n2, n3, n4);

          // Add childs if they contain points
          if(n1.vKeys.size() > 0)
          {
            lNodes.push_front(n1);
            if(n1.vKeys.size() > 1)
            {
              nToExpand++;
              vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(), &lNodes.front()));
              lNodes.front().lit = lNodes.begin();
            }
          }
          if(n2.vKeys.size() > 0)
          {
            lNodes.push_front(n2);
            if(n2.vKeys.size() > 1)
            {
              nToExpand++;
              vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(), &lNodes.front()));
              lNodes.front().lit = lNodes.begin();
            }
          }
          if(n3.vKeys.size() > 0)
          {
            lNodes.push_front(n3);
            if(n3.vKeys.size() > 1)
            {
              nToExpand++;
              vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(), &lNodes.front()));
              lNodes.front().lit = lNodes.begin();
            }
          }
          if(n4.vKeys.size() > 0)
          {
            lNodes.push_front(n4);
            if(n4.vKeys.size() > 1)
            {
              nToExpand++;
              vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(), &lNodes.front()));
              lNodes.front().lit = lNodes.begin();
            }
          }

          lit = lNodes.erase(lit);
          continue;
        }
      }

      // Finish if there are more nodes than required features
      // or all nodes contain just one point
      if((int) lNodes.size() >= N || (int) lNodes.size() == prevSize)
      {
        bFinish = true;
      }
      else if(((int) lNodes.size() + nToExpand * 3) > N)
      {

        while(!bFinish)
        {

          prevSize = lNodes.size();

          vector<pair<int, ExtractorNode *> > vPrevSizeAndPointerToNode = vSizeAndPointerToNode;
          vSizeAndPointerToNode.clear();

          sort(vPrevSizeAndPointerToNode.begin(), vPrevSizeAndPointerToNode.end());
          for(int j = vPrevSizeAndPointerToNode.size() - 1; j >= 0; j--)
          {
            ExtractorNode n1, n2, n3, n4;
            vPrevSizeAndPointerToNode[j].second->DivideNode(n1, n2, n3, n4);

            // Add childs if they contain points
            if(n1.vKeys.size() > 0)
            {
              lNodes.push_front(n1);
              if(n1.vKeys.size() > 1)
              {
                vSizeAndPointerToNode.push_back(make_pair(n1.vKeys.size(), &lNodes.front()));
                lNodes.front().lit = lNodes.begin();
              }
            }
            if(n2.vKeys.size() > 0)
            {
              lNodes.push_front(n2);
              if(n2.vKeys.size() > 1)
              {
                vSizeAndPointerToNode.push_back(make_pair(n2.vKeys.size(), &lNodes.front()));
                lNodes.front().lit = lNodes.begin();
              }
            }
            if(n3.vKeys.size() > 0)
            {
              lNodes.push_front(n3);
              if(n3.vKeys.size() > 1)
              {
                vSizeAndPointerToNode.push_back(make_pair(n3.vKeys.size(), &lNodes.front()));
                lNodes.front().lit = lNodes.begin();
              }
            }
            if(n4.vKeys.size() > 0)
            {
              lNodes.push_front(n4);
              if(n4.vKeys.size() > 1)
              {
                vSizeAndPointerToNode.push_back(make_pair(n4.vKeys.size(), &lNodes.front()));
                lNodes.front().lit = lNodes.begin();
              }
            }

            lNodes.erase(vPrevSizeAndPointerToNode[j].second->lit);

            if((int) lNodes.size() >= N)
              break;
          }

          if((int) lNodes.size() >= N || (int) lNodes.size() == prevSize)
            bFinish = true;

        }
      }
    }

    // Retain the best point in each node
    vector<cv::KeyPoint> vResultKeys;
    vResultKeys.reserve(nfeatures);
    for(list<ExtractorNode>::iterator lit = lNodes.begin(); lit != lNodes.end(); lit++)
    {
      vector<cv::KeyPoint>& vNodeKeys = lit->vKeys;
      cv::KeyPoint *pKP = &vNodeKeys[0];
      float maxResponse = pKP->response;

      for(size_t k = 1; k < vNodeKeys.size(); k++)
      {
        if(vNodeKeys[k].response > maxResponse)
        {
          pKP         = &vNodeKeys[k];
          maxResponse = vNodeKeys[k].response;
        }
      }

      vResultKeys.push_back(*pKP);
    }

    return vResultKeys;
  }

  void ORBextractor::ComputeKeyPointsOctTree(vector<vector<KeyPoint> >& allKeypoints, InputArray mask)
  {
    allKeypoints.resize(nlevels);

    const float W = 30;

    for(int level = 0; level < nlevels; ++level)
    {
      const int minBorderX = EDGE_THRESHOLD - 3;
      const int minBorderY = minBorderX;
      const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD + 3;
      const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD + 3;

      vector<cv::KeyPoint> vToDistributeKeys;
      vToDistributeKeys.reserve(nfeatures * 10);

      const float width  = (maxBorderX - minBorderX);
      const float height = (maxBorderY - minBorderY);

      const int nCols = width / W;
      const int nRows = height / W;
      const int wCell = ceil(width / nCols);
      const int hCell = ceil(height / nRows);

      float                          scale    = mvScaleFactor[level];

      cv::Mat const mask_ = mask.getMat();

      for(int       i     = 0; i < nRows; i++)
      {
        const float iniY = minBorderY + i * hCell;
        float       maxY = iniY + hCell + 6;

        if(iniY >= maxBorderY - 3)
          continue;
        if(maxY > maxBorderY)
          maxY = maxBorderY;

        for(int j = 0; j < nCols; j++)
        {
          const float iniX = minBorderX + j * wCell;
          float       maxX = iniX + wCell + 6;
          if(iniX >= maxBorderX - 6)
            continue;
          if(maxX > maxBorderX)
            maxX = maxBorderX;

          vector<cv::KeyPoint> vKeysCell;
          FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX), vKeysCell, iniThFAST, true);


          if(vKeysCell.empty())
          {
            FAST(mvImagePyramid[level].rowRange(iniY, maxY).colRange(iniX, maxX), vKeysCell, minThFAST, true);
          }

          if(!vKeysCell.empty())
          {
            for(vector<cv::KeyPoint>::iterator vit = vKeysCell.begin(); vit != vKeysCell.end(); vit++)
            {
              (*vit).pt.x += j * wCell;
              (*vit).pt.y += i * hCell;
              vToDistributeKeys.push_back(*vit);
            }
          }

        }
      }

      vector<KeyPoint>& keypoints = allKeypoints[level];
      keypoints.reserve(nfeatures);

      keypoints = DistributeOctTree(vToDistributeKeys,
                                    minBorderX,
                                    maxBorderX,
                                    minBorderY,
                                    maxBorderY,
                                    mnFeaturesPerLevel[level],
                                    level);

      const int scaledPatchSize = PATCH_SIZE * mvScaleFactor[level];

      // Add border to coordinates and scale information
      const int nkps = keypoints.size();
      for(int   i    = 0; i < nkps; i++)
      {
        keypoints[i].pt.x += minBorderX;
        keypoints[i].pt.y += minBorderY;
        keypoints[i].octave = level;
        keypoints[i].size = scaledPatchSize;
      }

      if(!mask.empty())
      {
        keypoints.erase(std::remove_if(keypoints.begin(), keypoints.end(), [&](cv::KeyPoint const& kp)
        {
          return mask_.at<std::uint8_t>(kp.pt*scale) == 0;
        }), keypoints.end());

      }
    }

    // compute orientations
    for(int level = 0; level < nlevels; ++level)
      computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
  }

  void ORBextractor::ComputeKeyPointsOld(std::vector<std::vector<KeyPoint> >& allKeypoints)
  {
    allKeypoints.resize(nlevels);

    float imageRatio = (float) mvImagePyramid[0].cols / mvImagePyramid[0].rows;

    for(int level = 0; level < nlevels; ++level)
    {
      const int nDesiredFeatures = mnFeaturesPerLevel[level];

      const int levelCols = sqrt((float) nDesiredFeatures / (5 * imageRatio));
      const int levelRows = imageRatio * levelCols;

      const int minBorderX = EDGE_THRESHOLD;
      const int minBorderY = minBorderX;
      const int maxBorderX = mvImagePyramid[level].cols - EDGE_THRESHOLD;
      const int maxBorderY = mvImagePyramid[level].rows - EDGE_THRESHOLD;

      const int W     = maxBorderX - minBorderX;
      const int H     = maxBorderY - minBorderY;
      const int cellW = ceil((float) W / levelCols);
      const int cellH = ceil((float) H / levelRows);

      const int nCells                    = levelRows * levelCols;
      const int nfeaturesCell             = ceil((float) nDesiredFeatures / nCells);

      vector<vector<vector<KeyPoint> > > cellKeyPoints(levelRows, vector<vector<KeyPoint> >(levelCols));

      vector<vector<int> >  nToRetain(levelRows, vector<int>(levelCols, 0));
      vector<vector<int> >  nTotal(levelRows, vector<int>(levelCols, 0));
      vector<vector<bool> > bNoMore(levelRows, vector<bool>(levelCols, false));
      vector<int>           iniXCol(levelCols);
      vector<int>           iniYRow(levelRows);
      int                   nNoMore       = 0;
      int                   nToDistribute = 0;


      float hY = cellH + 6;

      for(int i = 0; i < levelRows; i++)
      {
        const float iniY = minBorderY + i * cellH - 3;
        iniYRow[i] = iniY;

        if(i == levelRows - 1)
        {
          hY = maxBorderY + 3 - iniY;
          if(hY <= 0)
            continue;
        }

        float hX = cellW + 6;

        for(int j = 0; j < levelCols; j++)
        {
          float iniX;

          if(i == 0)
          {
            iniX = minBorderX + j * cellW - 3;
            iniXCol[j] = iniX;
          }
          else
          {
            iniX = iniXCol[j];
          }


          if(j == levelCols - 1)
          {
            hX = maxBorderX + 3 - iniX;
            if(hX <= 0)
              continue;
          }


          Mat cellImage = mvImagePyramid[level].rowRange(iniY, iniY + hY).colRange(iniX, iniX + hX);

          cellKeyPoints[i][j].reserve(nfeaturesCell * 5);

          FAST(cellImage, cellKeyPoints[i][j], iniThFAST, true);

          if(cellKeyPoints[i][j].size() <= 3)
          {
            cellKeyPoints[i][j].clear();

            FAST(cellImage, cellKeyPoints[i][j], minThFAST, true);
          }


          const int nKeys = cellKeyPoints[i][j].size();
          nTotal[i][j] = nKeys;

          if(nKeys > nfeaturesCell)
          {
            nToRetain[i][j] = nfeaturesCell;
            bNoMore[i][j]   = false;
          }
          else
          {
            nToRetain[i][j] = nKeys;
            nToDistribute += nfeaturesCell - nKeys;
            bNoMore[i][j] = true;
            nNoMore++;
          }

        }
      }


      // Retain by score

      while(nToDistribute > 0 && nNoMore < nCells)
      {
        int nNewFeaturesCell = nfeaturesCell + ceil((float) nToDistribute / (nCells - nNoMore));
        nToDistribute = 0;

        for(int i = 0; i < levelRows; i++)
        {
          for(int j = 0; j < levelCols; j++)
          {
            if(!bNoMore[i][j])
            {
              if(nTotal[i][j] > nNewFeaturesCell)
              {
                nToRetain[i][j] = nNewFeaturesCell;
                bNoMore[i][j]   = false;
              }
              else
              {
                nToRetain[i][j] = nTotal[i][j];
                nToDistribute += nNewFeaturesCell - nTotal[i][j];
                bNoMore[i][j] = true;
                nNoMore++;
              }
            }
          }
        }
      }

      vector<KeyPoint>& keypoints = allKeypoints[level];
      keypoints.reserve(nDesiredFeatures * 2);

      const int scaledPatchSize = PATCH_SIZE * mvScaleFactor[level];

      // Retain by score and transform coordinates
      for(int i = 0; i < levelRows; i++)
      {
        for(int j = 0; j < levelCols; j++)
        {
          vector<KeyPoint>& keysCell = cellKeyPoints[i][j];
          KeyPointsFilter::retainBest(keysCell, nToRetain[i][j]);
          if((int) keysCell.size() > nToRetain[i][j])
            keysCell.resize(nToRetain[i][j]);


          for(size_t k = 0, kend = keysCell.size(); k < kend; k++)
          {
            keysCell[k].pt.x += iniXCol[j];
            keysCell[k].pt.y += iniYRow[i];
            keysCell[k].octave = level;
            keysCell[k].size = scaledPatchSize;
            keypoints.push_back(keysCell[k]);
          }
        }
      }

      if((int) keypoints.size() > nDesiredFeatures)
      {
        KeyPointsFilter::retainBest(keypoints, nDesiredFeatures);
        keypoints.resize(nDesiredFeatures);
      }
    }

    // and compute orientations
    for(int level = 0; level < nlevels; ++level)
      computeOrientation(mvImagePyramid[level], allKeypoints[level], umax);
  }

  static void
  computeDescriptors(const Mat& image, vector<KeyPoint>& keypoints, Mat& descriptors, const vector<Point>& pattern)
  {
    descriptors = Mat::zeros((int) keypoints.size(), 32, CV_8UC1);

    for(size_t i = 0; i < keypoints.size(); i++)
      computeOrbDescriptor(keypoints[i], image, &pattern[0], descriptors.ptr((int) i));
  }

  void
  ORBextractor::operator()(InputArray _image, InputArray _mask, vector<KeyPoint>& _keypoints, OutputArray _descriptors)
  {
    if(_image.empty())
      return;

    Mat image = _image.getMat();
    assert(image.type() == CV_8UC1);

    // Pre-compute the scale pyramid
    ComputePyramid(image);

    vector<vector<KeyPoint> > allKeypoints;
    ComputeKeyPointsOctTree(allKeypoints, _mask);
    //ComputeKeyPointsOld(allKeypoints);

    Mat descriptors;

    int     nkeypoints = 0;
    for(int level      = 0; level < nlevels; ++level)
      nkeypoints += (int) allKeypoints[level].size();
    if(nkeypoints == 0)
      _descriptors.release();
    else
    {
      _descriptors.create(nkeypoints, 32, CV_8U);
      descriptors = _descriptors.getMat();
    }

    _keypoints.clear();
    _keypoints.reserve(nkeypoints);

    int     offset = 0;
    for(int level  = 0; level < nlevels; ++level)
    {
      vector<KeyPoint>& keypoints = allKeypoints[level];
      int nkeypointsLevel = (int) keypoints.size();

      if(nkeypointsLevel == 0)
        continue;

      // preprocess the resized image
      Mat workingMat = mvImagePyramid[level].clone();
      GaussianBlur(workingMat, workingMat, Size(7, 7), 2, 2, BORDER_REFLECT_101);

      // Compute the descriptors
      Mat desc = descriptors.rowRange(offset, offset + nkeypointsLevel);
      computeDescriptors(workingMat, keypoints, desc, pattern);

      offset += nkeypointsLevel;

      // Scale keypoint coordinates
      if(level != 0)
      {
        float                          scale    = mvScaleFactor[level]; //getScale(level, firstLevel, scaleFactor);
        for(vector<KeyPoint>::iterator keypoint = keypoints.begin(), keypointEnd = keypoints.end();
            keypoint != keypointEnd; ++keypoint)
          keypoint->pt *= scale;
      }
      // And add the keypoints to the output
      _keypoints.insert(_keypoints.end(), keypoints.begin(), keypoints.end());
    }
  }

  void ORBextractor::ComputePyramid(cv::Mat image)
  {
    for(int level = 0; level < nlevels; ++level)
    {
      float scale = mvInvScaleFactor[level];
      Size  sz(cvRound((float) image.cols * scale), cvRound((float) image.rows * scale));
      Size  wholeSize(sz.width + EDGE_THRESHOLD * 2, sz.height + EDGE_THRESHOLD * 2);
      Mat   temp(wholeSize, image.type()), masktemp;
      mvImagePyramid[level] = temp(Rect(EDGE_THRESHOLD, EDGE_THRESHOLD, sz.width, sz.height));

      // Compute the resized image
      if(level != 0)
      {
        resize(mvImagePyramid[level - 1], mvImagePyramid[level], sz, 0, 0, INTER_LINEAR);

        copyMakeBorder(mvImagePyramid[level],
                       temp,
                       EDGE_THRESHOLD,
                       EDGE_THRESHOLD,
                       EDGE_THRESHOLD,
                       EDGE_THRESHOLD,
                       BORDER_REFLECT_101 + BORDER_ISOLATED);
      }
      else
      {
        copyMakeBorder(image, temp, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, EDGE_THRESHOLD, BORDER_REFLECT_101);
      }
    }

  }

} //namespace ORB_SLAM
